The IoT Solutions Space: Edge-Computing IoT … SESSIONS...The IoT Solutions Space: Edge-Computing...
Transcript of The IoT Solutions Space: Edge-Computing IoT … SESSIONS...The IoT Solutions Space: Edge-Computing...
The IoT Solutions Space: Edge-Computing IoT
architecture, the FAR EDGE ProjectJohn Soldatos ([email protected], @jsoldatos), Professor Athens Information Technology
Contributor: Solufy Blog (http://www.solufy.com/blog) & The Internet of All Things
(theinternetofallthings.com)
• Industrial IoT (IIoT) Applications are in the Cloud:
• Based conventional Cloud Models (IaaS, PaaS, SaaS)
• But also IIoT specific cloud models (e.g., Sensing as a Service,
Maintenance-as-a-Service)
Industrial IoT Applications in the Cloud
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Performance Capacity
Elasticity Utility-Driven
IoT in the Cloud
IaaS
Cloud of sensors and actuators
Business Model: Data/Sensor
provider
Access control to resources
PaaSMost widespread
nowadays (Public IoT
Clouds)
Access to data, not to hardware
Tools & facilities for app
development
SaaS
Built over PaaS
Specific application domains
Typical utility-based business
models
Industrial IoT Solutions in the Cloud: The State-of-the-Art
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General purpose public IoT cloud services, offered by IT vendors
• E.g., Microsoft’s Azure IoT Suite
• IBM’s Watson IoT platform
• SAP’s HANA Cloud platform with IoT support and extensions
• Amazon AWS IoT LogmeIN’s Xively platform
• Not tailored to specific verticals
• Scalable and cost-effective cloud infrastructures for IoT
IIoT services offered by leaders in industrial solutions
• E.g.., SIEMENS, Bosch, ABB etc.
• Partnerships between IIoT vendors and providers of IT (IoT/cloud) infrastructure services e.g., ABB & Microsoft partnership, Bosch’s IoT services run over various digital plumbing platforms such as Amazon’s
• Distinction of business roles
Limitations of IIoT in the Cloud
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Waste of bandwidth
• Not all IoT data need to be stored in Cloud
• Waste of bandwidth especially in large scale applications
Network latency
• Interactions with the Cloud are not network efficient
• Can be a problem for real-time application
Inefficient use of storage
• Information with limited (or even zero) business value is stored
• Typical example: Sensor data that does not change frequently (such as temperature information)
Limited flexibility to address privacy and
data protection
• All data stored to the Cloud
• No easy way to “isolate” private/personal data
Data “away” from users
• Not ideal for applications involving mobility and large scale deployment
• Higher latency and cost
When Cloud is not enough: Edge Computing to your
rescue
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Ed
ge
Co
mp
utin
g Move IoT data processing and actuation to the edge of the network
Introduce a layer of gateways (Edge Nodes) between the Cloud and the IoT devices
Ed
ge
No
de
Typ
es Depend on the scale and the nature of the deployment
Embedded controllers or IoT devices with processing capability
Computers
Clusters or small-scale data centers
Be
ne
fits Reduced latency for
real-time applications
Efficient use of bandwidth and storage resources
Improved scalability
Reduction in costs and energy consumption
Better privacy control
High Level Concept
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• Standards:
• OpenFog Consortium &
OpenFog Reference
Architecture
• Industrial Internet Consortium
(IIC) and Industrial Internet
Consortium Architecture
• Implementations:
• IIC’s Edge Intelligence
Testbed
• EdgeX Foundry (Dell/EMC)
Edge Computing Standards & Reference Implementations for
IIoT
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FAR-EDGE = Joint effort of global leaders in manufacturing and IoT towards adoption of virtualized Factory Automation
•Cloud and Edge Computing for Manufacturing
•Decentralization of control
•RAMI 4.0 & Industrial Internet standards
Expected Outcomes
•Reduced Time to deploy new automation concepts and technologies (e.g., 3D printers)
•Better Exploitation of Data
• Increase automation in factories
• Improve process agility
•Enable x-factory collaboration
•RAMI Compliant Implementation
Advisory Board
Member
FAR-EDGE Aligns to
RAMI4.0: Common
Language for I4.0
(work-in-progress)
FAR-EDGE Vision and Unique Selling Proposition
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Vision: The vision of FAR-EDGE is to research & provide a first-of-a-kind Industrie 4.0 compliant factory automation (FA) platform based on the edge computing paradigm, which will deliver the benefits of the decentralized automation and without compromising the production quality, time and cost offered by existing platforms.
USP: FAR-EDGE goes beyond existing edge computing efforts for FA (E.g., IIC Edge Intelligence Testbeds) based on the use of blockchain and “smart contracts” for flexibly orchestrating factory automation tasks across plants and factories
FAR-EDGE Architecture: High Level Logical View
(Layered)
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FAR-EDGE Analytics Services
11H2020 Research & Innovation Action - This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N. 723094Device / Machine
Unit / Plant
Site / Factory
Cloud / Enterprise
Inter-Site Databus
(WAN / Internet)
E
D
G
E
C
O
M
P
U
T
I
N
G
Data Stream API
DAEDALUS
Edge Analytics
RTE Host process runs on server-class hardware.
Provides a runtime environment for Edge Analytics.
Internally, the RTE provides services for subscribing
to Machine-level Data Streams and for publishing
local results to APi4Analytics and/or SSS.
S S S
Secure State
Sharing Blockchain peer nodes run on cloud hardware.
Collection and aggregation of Site Analytics results
from individual “trusted” Sites.
Runs on PLC-class hardware.
Provides Data Stream Manifest Catalogue and
IoT Devices
(MQTT & OPC
UA)
Connectivity MiddlewareOne-to-one integration with IoT Devices and
DAEDALUS-powered Machines (not a
bus/aggregator), presents a unified Data Stream API.
Open API
for Analytics
Connectivity Middleware
•Interfaces to the Field through several standards-based protocols (OPC-UA, MQTT,…) and automation frameworks IEC 61499
•Homogenizes data according to FAR-EDGE Data Models
•Provides services to “upper” layers in a way that hides the connectivity protocol used
•Provides data “filtering” functionalities
•Note: According to IIC Connectivity is a Cross-Cutting Function
Data Routing
•Enables the combination, fusion and routing of multiple data streams from field devices
•It typically “consumes” data from the connectivity middleware
•Delivers combined streams to higher layer via a Message Bus infrastructure
Synchronization
•Keeps the Digital Representation of the Plant (Plant Description, CPS Models, Simulation Models) in synch with the physical world
•In FAR-EDGE it will most likely deal with a subset of the factory “mini-world” relevant to the use cases
•Runs independently of scenarios and other functionalities (as “framework service”)
Reconfiguration
•Leverages the synchronization capabilities in order to reconfigure the digital world based on changes to the physical world
•Handles complete processes (including batches of synchronization steps)
•Triggered from changes in the physical world (connectivity middleware layer) or the blockchain
FAR-EDGE Layers & Elements (1)
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FAR-EDGE Database / Datastore:
• Reflects a digital representation of the plant in terms of FAR-EDGE operations & applications
• Comprised of Metadata = FAR-EDGE CPS Models & Data = Reflecting the actual status of the plant
Service Registry
• Provides dynamic information about the status of the plant, along with dynamic bindings & handles to FAR-EDGE services (Service Oriented Approach)
Automation
• Implements automation workflows leveraging secure state sharing services
• In the scope of an automation workflow it can change and validate the status of objects and processes
Analytics
• Implements analytics workflows & pipelines based on rules configured in the blockchain
• Keeps track, configures and evolves the state of analytics rules in order to implement data analytics logic over shopfloor data
FAR-EDGE Layers & Elements (2)
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Secure State Sharing SSS
• Powered by blockchain – distributed ledger
• Provides the means for distributed configuration and validation of automation and analytics rules
• Provides the means for tracking and evolving the state of objects and processes involved in the automation
• Empowers analytics and automation
• Should be also used for the reconfiguration (even though reconfiguration box is outside SSS)
Simulation
• Leverages data from the connectivity middleware via data routing
• Exploits persistence services over the CPS models & Plant Description models (data & metadata)
• Implemented based on simulation engines from SIEMENS & SUPSI
FAR-EDGE Layers & Elements (3)
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Example Physical View
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FAR-EDGE Implementation Technologies (1)
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Component Implementation Technology
Connectivity
Middleware
• Arrowhead or Open Source frameworks for MQTT, OPC UA
• Custom Drivers to Factory Databases in WHR / VTC
• FAR-EDGE DAEDALUS interface for IEC 61499
Data Routing • Apache Edgent for supporting CPU constrained devices
• Apache Kafka or Alternative Distributed Streaming
Technology
FAR-EDGE
Database &
Service Registry
• Data Schemas AutomationML (Custom/MAYA), SenseML
(Arrowhead), B2MML (Custom)
• Service Registry (Arrowhead)
• Data Management (Custom, Arrowhead Historian, BigData
Framework e.g. Spark)
FAR-EDGE Implementation Technologies (2)
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Component Implementation Technology
Secure State Sharing • Hyperledger Fabric
Automation & Analytics • Configuration Protocols and Rules over the Fabric
Simulation • Timeseries models (SUPSI)
• Simulation Engine (SIEMENS)
Synchronization • Custom implementation possibly extending Arrowhead
mechanisms
Security • NGAC on Policy Enforcement Points (PEP)
Reconfiguration • Configuration Protocols and Rules over the Fabric
• Edge Computing provides compelling advantages for several IIOT use
cases
• Especially when edge analytics and interactions “close” to the field
are essential
• Evident also in standards
• The industry is seeking for reference implementations and “best
practices”
• FAR-EDGE contributes in this direction
• It is a reference implementation of an edge computing architecture
• It also “researches” blockchain-empowered automation & analytics
Conclusions
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